机器学习在功能梯度材料设计-制备中的应用综述

doi:10.11896/cldb.22030237

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Abstract Functionally graded materials as a novel type of composite material have great application value in many advanced fields such as aerospace, biomedicine, etc. However, the high-dimensional and complex problems existing in the process of design and fabrication restrict their rapid development. The rapid rise of big data-driven artificial intelligence technologies is driving the transformation of traditional research and development (R&D) models to the digital R&D. Digital R&D technologies can address the complexities and uncertainties in the design and fabrication of functionally graded materials, and it can significantly improve product quality, production efficiency and reduce costs, and it is rapidly driving the development of functionally graded materials in a number of advanced fields. This paper focused on recent research in the field of functionally graded materials using machine learning techniques, and summarized the integration of different optimization methods to solve complex problems in the design and preparation of functionally graded materials. It contains the accurate inversion of material components information with different dimensional variations;the prediction and evaluation of natural properties, microstructure, material characteristics and service performance of functionally graded material parts;the determination and optimization of material component information and preparation process parameters based on single or multiple objectives;establishment of intelligent databases with explanation based on the data-driven method, which provide a new design idea for obtaining functionally graded materials with superior properties. Moreover, the main challenges and opportunities of machine learning techniques in the field of functionally graded materials were summarized in this paper.

Key words： functionally graded material big data-driven digital R&D machine learning

Published: 10 November 2023 Online: 2023-11-10

CLC number:

TB34

Fund:Central Guidance for Local Science and Technology Development Support Program (216Z1804G).

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	Articles by authors
	WANG Shijie
	YANG Jie
	MA Shuo
	HAN Shuo
	WANG Long
	DUAN GuoLin

Cite this article:

WANG Shijie,YANG Jie,MA Shuo, et al. A Review:Applications of Machine Learning in Design-Fabrication of Functionally Graded Materials[J]. Materials Reports, 2023, 37(21): 22030237-9.

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http://www.mater-rep.com/EN/10.11896/cldb.22030237 OR http://www.mater-rep.com/EN/Y2023/V37/I21/22030237

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